Transcript

Machine Dreams - Episode 15 Trailer · Machine Dreams

Kolawole Samuel Adebayo: you don't necessarily need to input data the same way you did before. The data now becomes automated. Now you're going to hear data as a service, and we use that as well. But the real question is, how do you actually then use that? And how does an organization actually get value from using more data? So when you say data as a service, you're talking about turning conversations like voice calls and text exchanges into something a bank or an HR team can act on. How does that work? What are you pulling out of those conversations that really matter? We are, through our technology, listening to kind of the behavioral aspects of how we speak. There's a footprint in terms of individually, in terms of how we speak. And we've been able to tie this up to kind of processing and looking at the machine learning of this over years and years of data points to then see, can we pull common threads? in terms of to link how we speak to, let's say, risk management. I know that when it comes to detection of some type of predictive capabilities or some AI-driven prediction outcomes, there's always the issue of bias, right? How do you deal with bias? We are not looking at the words that you say or the education or am I ⁓ using Oxford English? We work in every major language in the world. and our base model, because we're not looking at the words, can cut through those different elements. And we believe take out those biases that we might have all as individuals.